Leveraging Multimodal Haptic Sensory Data for Robust Cutting
This work addresses the problem of robust robotic manipulation for cooking tasks, which is incremental as it builds on existing haptic feedback methods.
The paper tackled the challenge of robotic cutting across diverse food materials by using multimodal haptic sensory data to adapt slicing motions and monitor contact events, resulting in reliable cutting of over 20 food types and detection of food freshness.
Cutting is a common form of manipulation when working with divisible objects such as food, rope, or clay. Cooking in particular relies heavily on cutting to divide food items into desired shapes. However, cutting food is a challenging task due to the wide range of material properties exhibited by food items. Due to this variability, the same cutting motions cannot be used for all food items. Sensations from contact events, e.g., when placing the knife on the food item, will also vary depending on the material properties, and the robot will need to adapt accordingly. In this paper, we propose using vibrations and force-torque feedback from the interactions to adapt the slicing motions and monitor for contact events. The robot learns neural networks for performing each of these tasks and generalizing across different material properties. By adapting and monitoring the skill executions, the robot is able to reliably cut through more than 20 different types of food items and even detect whether certain food items are fresh or old.